In general, autonomous vehicles have used control system architectures to perform autonomous driving functions to travel a trajectory plan having an origin and a destination. To accomplish the larger objective of traveling from the origin to the destination, the trajectory plan has been parsed into smaller objectives, such as “turn” at a specific landmark, to travel at a speed within a designated limit, etc. Other vehicles, which may not be fully or partially autonomous, have not had the capability to share trajectory plans and/or to otherwise coordinate travel routes with autonomous vehicles on a roadway. A strategy has been to consider these other vehicles as part of a stationary environment relative to the autonomous vehicle - just that the environment changed more frequently. Also, the high sampling and scanning rates for assessing these environments yielded a large data volume to process and access in a time threshold sufficient to effectively allow timely autonomous vehicle decisions. A need exists for a method and device that can optimize autonomous vehicle decision making in a multi-vehicle environment so as to form timely action decisions (such as whether to accelerate, decelerate, etc.) while also taking into account the non-constant, or non-static, operation of other vehicles in the multi-vehicle environment.